{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter 5 Descriptive Statistics.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_vdqTd18EtY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LcaVuzbb1IB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Math, Latex\n",
        "from IPython.core.display import Image\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "sns.set(rc={'figure.figsize':(10,6)})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivBEuWKDcSyK",
        "colab_type": "code",
        "outputId": "e643a282-6cb4-437e-c277-186be2f4ff4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "# Uniform Distribution\n",
        "from scipy.stats import uniform\n",
        "\n",
        "number = 10000\n",
        "start = 20\n",
        "width = 25\n",
        "\n",
        "uniform_data = uniform.rvs(size=number, loc=start, scale=width)\n",
        "axis = sns.distplot(uniform_data, bins=100, kde=True, color='skyblue', hist_kws={\"linewidth\": 15})\n",
        "axis.set(xlabel='Uniform Distribution ', ylabel='Frequency')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Uniform Distribution ')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T+OUocGmDg2taI2iNLd99s1TVfYdr3paEMQYjKY1Xp1y8OumiP+F3o1kC6IhZuKo1gvao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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sDufozNdtCe",
        "colab_type": "code",
        "outputId": "aac5932e-370c-4d97-a53b-6f7c4a1ed425",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "# Normal distribution\n",
        "from scipy.stats import norm\n",
        "\n",
        "normal_data = norm.rvs(size=90000,loc=20,scale=30)\n",
        "axis = sns.distplot(normal_data, bins=100, kde=True, color='skyblue', hist_kws={\"linewidth\": 15,'alpha':0.568})\n",
        "axis.set(xlabel='Normal Distribution', ylabel='Frequency')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Normal Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Th3WbOoVat1nRFUtElGqnR3VMmDKnW+viKicnUZwcWtyxdESU/xjYEVHOk1Lif4YiqHCq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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlN-eReOe9Y4",
        "colab_type": "code",
        "outputId": "0f3050c2-15ac-48e8-e1b2-5933e57afb00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "# Gamma distribution\n",
        "from scipy.stats import gamma\n",
        "\n",
        "gamma_data = gamma.rvs(a=5, size=10000)\n",
        "axis = sns.distplot(gamma_data, kde=True, bins=100, color='skyblue', hist_kws={\"linewidth\": 15})\n",
        "axis.set(xlabel='Example of Gamma Distribution', ylabel='Frequency')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Example of Gamma Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5NZQmZDEnJyW+kk9UuFjAr8+pmVVERKZOgZ0Eyqkhj1hpCOcKa8PORSUhm8VlIY4NpEgV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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43l1Bp7efgVE",
        "colab_type": "code",
        "outputId": "e3968bec-eb5a-48cf-b82c-b47e574239e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "# Exponential distribution\n",
        "from scipy.stats import expon\n",
        "\n",
        "expon_data = expon.rvs(scale=1,loc=0,size=1000)\n",
        "axis = sns.distplot(expon_data, kde=True, bins=100, color='skyblue', hist_kws={\"linewidth\": 15})\n",
        "axis.set(xlabel='Exponential Distribution', ylabel='Frequency')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Exponential Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ehh2FsCmKtgfdiZoRMNAS4jAnERFRvjCg+VDU9XcVgfGcVmvicMxFxOYwJxER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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-uVyOXtfzEH",
        "colab_type": "code",
        "outputId": "456450b2-d9be-4eb6-e235-807c351aa5df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "# Poisson Distribution\n",
        "from scipy.stats import poisson\n",
        "\n",
        "poisson_data = poisson.rvs(mu=2, size=10000)\n",
        "axis = sns.distplot(poisson_data, bins=30, kde=False, color='red', hist_kws={\"linewidth\": 15})\n",
        "axis.set(xlabel='Poisson Distribution', ylabel='Frequency')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Poisson Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KFy9ebHFtXGlpqeLi4jzafvXq1dq/f7/Onz+vBx54QDExMcrLy9P8+fPlcDjkdDo1fPhw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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAotunfQyBO7",
        "colab_type": "code",
        "outputId": "3f440f8d-6f62-4030-cd9a-330f0cdfced3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "from scipy.stats import binom\n",
        "\n",
        "binomial_data = binom.rvs(n=10, p=0.8,size=10000)\n",
        "\n",
        "axis = sns.distplot(binomial_data, kde=False, color='red', hist_kws={\"linewidth\": 15})\n",
        "axis.set(xlabel='Binomial Distribution', ylabel='Frequency')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Binomial Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xsXJzc5WXlxfukgAAAELG2CBXWVmp3bt3Kzs7W5KUnZ2t3bt3q6qqKsyVAQAA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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ccn8bquu4LE",
        "colab_type": "code",
        "outputId": "68626f3f-669b-4535-812e-862e9e95bf1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "# loading data set as Pandas dataframe\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/PacktPublishing/hands-on-exploratory-data-analysis-with-python/master/Chapter%205/data.csv\")\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num-of-doors</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>fuel-system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling normalized-losses         make  ... city-mpg highway-mpg  price\n",
              "0          3                 ?  alfa-romero  ...       21          27  13495\n",
              "1          3                 ?  alfa-romero  ...       21          27  16500\n",
              "2          1                 ?  alfa-romero  ...       19          26  16500\n",
              "3          2               164         audi  ...       24          30  13950\n",
              "4          2               164         audi  ...       18          22  17450\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrCdNtnKOiVe",
        "colab_type": "code",
        "outputId": "c20392c8-6524-428f-857b-51724d0fbb09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "symboling              int64\n",
              "normalized-losses     object\n",
              "make                  object\n",
              "fuel-type             object\n",
              "aspiration            object\n",
              "num-of-doors          object\n",
              "body-style            object\n",
              "drive-wheels          object\n",
              "engine-location       object\n",
              "wheel-base           float64\n",
              "length               float64\n",
              "width                float64\n",
              "height               float64\n",
              "curb-weight            int64\n",
              "engine-type           object\n",
              "num-of-cylinders      object\n",
              "engine-size            int64\n",
              "fuel-system           object\n",
              "bore                  object\n",
              "stroke                object\n",
              "compression-ratio    float64\n",
              "horsepower            object\n",
              "peak-rpm              object\n",
              "city-mpg               int64\n",
              "highway-mpg            int64\n",
              "price                 object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjbweKCCSZ6_",
        "colab_type": "text"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WT3hky4XEc7y",
        "colab_type": "code",
        "outputId": "4c16af26-55bb-4919-d5a8-95b4a5fbd4fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Find out the number of values which are not numeric\n",
        "df['price'].str.isnumeric().value_counts()\n",
        "\n",
        "# List out the values which are not numeric\n",
        "df['price'].loc[df['price'].str.isnumeric() == False]\n",
        "\n",
        "#Setting the missing value to mean of price and convert the datatype to integer\n",
        "price = df['price'].loc[df['price'] != '?']\n",
        "pmean = price.astype(str).astype(int).mean()\n",
        "df['price'] = df['price'].replace('?',pmean).astype(int)\n",
        "df['price'].head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    13495\n",
              "1    16500\n",
              "2    16500\n",
              "3    13950\n",
              "4    17450\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aGIcFuaNOnk",
        "colab_type": "code",
        "outputId": "327537d5-5869-4c2e-c0da-064fa17fcc3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Cleaning the horsepower losses field\n",
        "df['horsepower'].str.isnumeric().value_counts()\n",
        "horsepower = df['horsepower'].loc[df['horsepower'] != '?']\n",
        "hpmean = horsepower.astype(str).astype(int).mean()\n",
        "df['horsepower'] = df['horsepower'].replace('?',hpmean).astype(int)\n",
        "df['horsepower'].head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    111\n",
              "1    111\n",
              "2    154\n",
              "3    102\n",
              "4    115\n",
              "Name: horsepower, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3EMdTUUSrUq",
        "colab_type": "code",
        "outputId": "91af39f8-eb0b-463d-833d-6b7adc223294",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Cleaning the Normalized losses field\n",
        "df[df['normalized-losses']=='?'].count()\n",
        "nl=df['normalized-losses'].loc[df['normalized-losses'] !='?'].count()\n",
        "nmean=nl.astype(str).astype(int).mean()\n",
        "df['normalized-losses'] = df['normalized-losses'].replace('?',nmean).astype(int)\n",
        "df['normalized-losses'].head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    164\n",
              "1    164\n",
              "2    164\n",
              "3    164\n",
              "4    164\n",
              "Name: normalized-losses, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQRjtnUnS-wd",
        "colab_type": "code",
        "outputId": "5d437dd5-13e3-47ae-d49e-7286f956b92d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# cleaning the bore\n",
        "# Find out the number of invalid value\n",
        "df['bore'].loc[df['bore'] == '?']\n",
        "# Replace the non-numeric value to null and convert the datatype\n",
        "df['bore'] = pd.to_numeric(df['bore'],errors='coerce')\n",
        "df.bore.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3.47\n",
              "1    3.47\n",
              "2    2.68\n",
              "3    3.19\n",
              "4    3.19\n",
              "Name: bore, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp2uE8OzTOcp",
        "colab_type": "code",
        "outputId": "81e4c2e7-9faa-4867-a008-b1355dd3dca7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Cleaning the column stoke\n",
        "df['stroke'] = pd.to_numeric(df['stroke'],errors='coerce')\n",
        "df['stroke'].head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2.68\n",
              "1    2.68\n",
              "2    3.47\n",
              "3    3.40\n",
              "4    3.40\n",
              "Name: stroke, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vC5Ey5yuTb0H",
        "colab_type": "code",
        "outputId": "ee1a5b98-8253-4ab5-de97-827abb44a4dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Cleaning the column peak-rpm \n",
        "df['peak-rpm'] = pd.to_numeric(df['peak-rpm'],errors='coerce')\n",
        "df['peak-rpm'].head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5000.0\n",
              "1    5000.0\n",
              "2    5000.0\n",
              "3    5500.0\n",
              "4    5500.0\n",
              "Name: peak-rpm, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjEWKFd6Tug9",
        "colab_type": "code",
        "outputId": "26086dad-c41e-420c-c3b6-05a3c1f0fbb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Cleaning the Column num-of-doors data\n",
        "# remove the records which are having the value '?'\n",
        "df['num-of-doors'].loc[df['num-of-doors'] == '?']\n",
        "df= df[df['num-of-doors'] != '?']\n",
        "df['num-of-doors'].loc[df['num-of-doors'] == '?']"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: num-of-doors, dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAnEQAKQTof9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rz3m8rQb9gmw",
        "colab_type": "code",
        "outputId": "948f737d-0945-4361-e060-70da9b5950bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.00000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.837438</td>\n",
              "      <td>130.147783</td>\n",
              "      <td>98.781281</td>\n",
              "      <td>174.11330</td>\n",
              "      <td>65.915271</td>\n",
              "      <td>53.731527</td>\n",
              "      <td>2557.916256</td>\n",
              "      <td>127.073892</td>\n",
              "      <td>3.330955</td>\n",
              "      <td>3.254070</td>\n",
              "      <td>10.093202</td>\n",
              "      <td>104.463054</td>\n",
              "      <td>5125.870647</td>\n",
              "      <td>25.172414</td>\n",
              "      <td>30.699507</td>\n",
              "      <td>13241.911330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.250021</td>\n",
              "      <td>35.956490</td>\n",
              "      <td>6.040994</td>\n",
              "      <td>12.33909</td>\n",
              "      <td>2.150274</td>\n",
              "      <td>2.442526</td>\n",
              "      <td>522.557049</td>\n",
              "      <td>41.797123</td>\n",
              "      <td>0.274054</td>\n",
              "      <td>0.318023</td>\n",
              "      <td>3.888216</td>\n",
              "      <td>39.612384</td>\n",
              "      <td>479.820136</td>\n",
              "      <td>6.529812</td>\n",
              "      <td>6.874645</td>\n",
              "      <td>7898.957924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>86.600000</td>\n",
              "      <td>141.10000</td>\n",
              "      <td>60.300000</td>\n",
              "      <td>47.800000</td>\n",
              "      <td>1488.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>2.540000</td>\n",
              "      <td>2.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>4150.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>5118.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>166.55000</td>\n",
              "      <td>64.100000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2145.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7781.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.20000</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>2414.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>3.290000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>5200.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10595.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>183.30000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>2943.500000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>3.590000</td>\n",
              "      <td>3.410000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>5500.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>16500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.10000</td>\n",
              "      <td>72.300000</td>\n",
              "      <td>59.800000</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>4.170000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>45400.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        symboling  normalized-losses  ...  highway-mpg         price\n",
              "count  203.000000         203.000000  ...   203.000000    203.000000\n",
              "mean     0.837438         130.147783  ...    30.699507  13241.911330\n",
              "std      1.250021          35.956490  ...     6.874645   7898.957924\n",
              "min     -2.000000          65.000000  ...    16.000000   5118.000000\n",
              "25%      0.000000         101.000000  ...    25.000000   7781.500000\n",
              "50%      1.000000         128.000000  ...    30.000000  10595.000000\n",
              "75%      2.000000         164.000000  ...    34.000000  16500.000000\n",
              "max      3.000000         256.000000  ...    54.000000  45400.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVvsxOBEu6i2",
        "colab_type": "text"
      },
      "source": [
        "Let's start by computing Measure of central tendency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xermW08E0_hP",
        "colab_type": "code",
        "outputId": "5d6946f1-4b63-46e5-d744-ab0eb54b8b26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "# get column height from df\n",
        "height =df[\"height\"]\n",
        "print(height)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0      48.8\n",
            "1      48.8\n",
            "2      52.4\n",
            "3      54.3\n",
            "4      54.3\n",
            "       ... \n",
            "200    55.5\n",
            "201    55.5\n",
            "202    55.5\n",
            "203    55.5\n",
            "204    55.5\n",
            "Name: height, Length: 203, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SdC7Vre1ysd",
        "colab_type": "code",
        "outputId": "35c9463a-4bd0-4179-856a-8ddbb29b2009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#calculate mean, median and mode of dat set height\n",
        "mean = height.mean()\n",
        "median =height.median()\n",
        "mode = height.mode()\n",
        "print(mean , median, mode)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "53.73152709359609 54.1 0    50.8\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cydMjbLD_g_D",
        "colab_type": "text"
      },
      "source": [
        "For categorical variables which has discrite values we can summarize the categorical data is by using the function value_counts(). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2GMtsoTOmzL",
        "colab_type": "code",
        "outputId": "64443e78-98a1-46c3-daf1-4667d15b67bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.make.value_counts().nlargest(30).plot(kind='bar', figsize=(14,8))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make of the cars');"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MBQAAAFjC52W3K1asqKZNm6pWrVqqXr06ZegaERoSpMQxGRe/YxmWTk9SXjnl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\nW08//bRSU1MtzbN3716tXLnS0jFL86c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j9PR0ztnZmVk8HMdxVVVVXFVVFdMYOI7jxo8f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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVakZvw-AMsR",
        "colab_type": "code",
        "outputId": "cd637ba7-2ed8-4d21-aaa9-d10ad89b223f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#summarize categories of drive-wheels\n",
        "drive_wheels_count =df[\"drive-wheels\"].value_counts()\n",
        "print(drive_wheels_count)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fwd    118\n",
            "rwd     76\n",
            "4wd      9\n",
            "Name: drive-wheels, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_MdTpnB8Tti",
        "colab_type": "text"
      },
      "source": [
        "**Measures of Dispersion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvVTTyil5tKB",
        "colab_type": "code",
        "outputId": "0573763f-10f9-4122-88a7-49cc3b1f4cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "#standard variance of data set using std() function\n",
        "std_dev =df.std()\n",
        "print(std_dev)\n",
        "# standard variance of the specific column\n",
        "sv_height=df.loc[:,\"height\"].std()\n",
        "print(sv_height)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "symboling               1.250021\n",
            "normalized-losses      35.956490\n",
            "wheel-base              6.040994\n",
            "length                 12.339090\n",
            "width                   2.150274\n",
            "height                  2.442526\n",
            "curb-weight           522.557049\n",
            "engine-size            41.797123\n",
            "bore                    0.274054\n",
            "stroke                  0.318023\n",
            "compression-ratio       3.888216\n",
            "horsepower             39.612384\n",
            "peak-rpm              479.820136\n",
            "city-mpg                6.529812\n",
            "highway-mpg             6.874645\n",
            "price                7898.957924\n",
            "dtype: float64\n",
            "2.442525704031867\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ete2HtKqKm_c",
        "colab_type": "text"
      },
      "source": [
        "# Measure of variance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyCfxqeBK_Hl",
        "colab_type": "code",
        "outputId": "f51ef9bd-8e83-4e60-f64f-6996badfa3f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "# variance of data set using var() function\n",
        "variance=df.var()\n",
        "print(variance)\n",
        "# variance of the specific column\n",
        "var_height=df.loc[:,\"height\"].var()\n",
        "print(var_height)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "symboling            1.562552e+00\n",
            "normalized-losses    1.292869e+03\n",
            "wheel-base           3.649361e+01\n",
            "length               1.522531e+02\n",
            "width                4.623677e+00\n",
            "height               5.965932e+00\n",
            "curb-weight          2.730659e+05\n",
            "engine-size          1.746999e+03\n",
            "bore                 7.510565e-02\n",
            "stroke               1.011384e-01\n",
            "compression-ratio    1.511822e+01\n",
            "horsepower           1.569141e+03\n",
            "peak-rpm             2.302274e+05\n",
            "city-mpg             4.263844e+01\n",
            "highway-mpg          4.726074e+01\n",
            "price                6.239354e+07\n",
            "dtype: float64\n",
            "5.965931814856368\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WldUDZbOYEAd",
        "colab_type": "code",
        "outputId": "438fefce-174e-47fb-c11c-cca1e7b007fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.loc[:,\"height\"].var()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.965931814856368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9ZmeTubK4vs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6t8NJKCJEWL",
        "colab_type": "code",
        "outputId": "73777de4-6c30-44e6-803e-9cf1c698c40a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "df.skew()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "symboling            0.204275\n",
              "normalized-losses    0.209007\n",
              "wheel-base           1.041170\n",
              "length               0.154086\n",
              "width                0.900685\n",
              "height               0.064134\n",
              "curb-weight          0.668942\n",
              "engine-size          1.934993\n",
              "bore                 0.013419\n",
              "stroke              -0.669515\n",
              "compression-ratio    2.682640\n",
              "horsepower           1.391224\n",
              "peak-rpm             0.073094\n",
              "city-mpg             0.673533\n",
              "highway-mpg          0.549104\n",
              "price                1.812335\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVUxKXAyZRc3",
        "colab_type": "code",
        "outputId": "5be7fef7-895b-4e8a-b474-7aeddf60d9ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# skewness of the specific column\n",
        "df.loc[:,\"height\"].skew()\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06413448813322854"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VG737ZQ6oxH3",
        "colab_type": "text"
      },
      "source": [
        "# Kurtosis\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2GuUXzUsNFX",
        "colab_type": "code",
        "outputId": "f38ac81a-ceff-48ee-8bde-b176660fa689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "# Kurtosis of data in data using skew() function\n",
        "kurtosis =df.kurt()\n",
        "print(kurtosis)\n",
        "\n",
        "# Kurtosis of the specific column\n",
        "sk_height=df.loc[:,\"height\"].kurt()\n",
        "print(sk_height)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "symboling           -0.691709\n",
            "normalized-losses   -0.471235\n",
            "wheel-base           0.986065\n",
            "length              -0.075680\n",
            "width                0.687375\n",
            "height              -0.429298\n",
            "curb-weight         -0.069648\n",
            "engine-size          5.233661\n",
            "bore                -0.830965\n",
            "stroke               2.030592\n",
            "compression-ratio    5.643878\n",
            "horsepower           2.646625\n",
            "peak-rpm             0.068155\n",
            "city-mpg             0.624470\n",
            "highway-mpg          0.479323\n",
            "price                3.287412\n",
            "dtype: float64\n",
            "-0.4292976016374439\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tg0BVF-lBbg1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()\n",
        "plt.rcParams['figure.figsize'] = (10, 6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0h60Cunf-xe9",
        "colab_type": "code",
        "outputId": "3cb5f44e-c145-4a8b-de75-0e81e25bfea9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "# plot the relationship between “engine-size” and ”price”\n",
        "plt.scatter(df[\"price\"], df[\"engine-size\"])\n",
        "plt.title(\"Scatter Plot for engine-size vs price\")\n",
        "plt.xlabel(\"engine-size\")\n",
        "plt.ylabel(\"price\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'price')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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frgMHDuiMM87QoEGDalzZrW3IkCGuY1555ZUeryTWVvU5zZo1y3W7t6pX7VVXXaUTJ07o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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccUEYiG131w0",
        "colab_type": "code",
        "outputId": "6a9ee374-c22f-41da-ae72-ea48eb1a5c04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "#boxplot to visualize the distribution of \"price\" with types of \"drive-wheels\"\n",
        "sns.boxplot(x=\"drive-wheels\", y=\"price\",data=df)\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c7d9c9dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uATANn5yTFxcXJ7vdftG20tJS7du3T8OHD5ckDR8+XPv27VNZWZlXagAAAIHE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\nAACACf0/xSmDpB6yCIMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q49nCnLU-7Uc",
        "colab_type": "code",
        "outputId": "0b32883c-c18c-4751-c2b8-50d952590b4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(df.price[0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aSC74OD8K6Jk"
      },
      "source": [
        "# Calculating percentiles\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1_yVU9kEXoR",
        "colab_type": "code",
        "outputId": "709daec3-de52-4866-abaf-08004cd0dfa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# calculating 30th percentile of heights in dataset\n",
        "height = df[\"height\"]\n",
        "percentile = np.percentile(height, 50,)\n",
        "print(percentile)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "54.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENzTKt-CJMGS",
        "colab_type": "text"
      },
      "source": [
        "**Quartiles** \n",
        "\n",
        "It divides the data set into four equal points. \n",
        "\n",
        "First quartile = 25th percentile\n",
        "Second quartile = 50th percentile (Median)\n",
        "Third quartile = 75th percentile\n",
        "\n",
        "Based on the quartile, there is a another measure called inter-quartile range that also measures the variability in the dataset. It is defined as:\n",
        "\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "IQR is not affected by the presence of outliers. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2qJhFB8aKyd",
        "colab_type": "code",
        "outputId": "03bd3f06-c8e6-4538-e204-213f9812ed65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "price = df.price.sort_values()\n",
        "Q1 = np.percentile(price, 25)\n",
        "Q2 = np.percentile(price, 50)\n",
        "Q3 = np.percentile(price, 75)\n",
        "\n",
        "IQR = Q3 - Q1\n",
        "IQR"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8718.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6ERrNg_6aD6",
        "colab_type": "code",
        "outputId": "3453a020-4a29-4d48-b3d5-50242a891eaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "df[\"normalized-losses\"].describe()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    203.000000\n",
              "mean     130.147783\n",
              "std       35.956490\n",
              "min       65.000000\n",
              "25%      101.000000\n",
              "50%      128.000000\n",
              "75%      164.000000\n",
              "max      256.000000\n",
              "Name: normalized-losses, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsNBWFzjZzOO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "scorePhysics = [34,35,35,35,35,35,36,36,37,37,37,37,37,38,38,38,39,39,\n",
        "              40,40,40,40,40,41,42,42,42,42,42,42,42,42,43,43,43,43,44,44,44,44,44,44,45,\n",
        "              45,45,45,45,46,46,46,46,46,46,47,47,47,47,47,47,48,48,48,48,48,49,49,49,49,\n",
        "              49,49,49,49,52,52,52,53,53,53,53,53,53,53,53,54,54,\n",
        "              54,54,54,54,54,55,55,55,55,55,56,56,56,56,56,56,57,57,57,58,58,59,59,59,59,\n",
        "              59,59,59,60,60,60,60,60,60,60,61,61,61,61,61,62,62,63,63,63,63,63,64,64,64,\n",
        "              64,64,64,64,65,65,65,66,66,67,67,68,68,68,68,68,68,68,69,70,71,71,71,72,72,\n",
        "              72,72,73,73,74,75,76,76,76,76,77,77,78,79,79,80,80,81,84,84,85,85,87,87,88]\n",
        "            \n",
        "scoreLiterature = [49,49,50,51,51,52,52,52,52,53,54,54,55,55,55,55,56,\n",
        "                 56,56,56,56,57,57,57,58,58,58,59,59,59,60,60,60,60,60,60,60,61,61,61,62,\n",
        "                 62,62,62,63,63,67,67,68,68,68,68,68,68,69,69,69,69,69,69,\n",
        "                 70,71,71,71,71,72,72,72,72,73,73,73,73,74,74,74,74,74,75,75,75,76,76,76,\n",
        "                 77,77,78,78,78,79,79,79,80,80,82,83,85,88]\n",
        "                 \n",
        "scoreComputer = [56,57,58,58,58,60,60,61,61,61,61,61,61,62,62,62,62,\n",
        "                63,63,63,63,63,64,64,64,64,65,65,66,66,67,67,67,67,67,67,67,68,68,68,69,\n",
        "                69,70,70,70,71,71,71,73,73,74,75,75,76,76,77,77,77,78,78,81,82,\n",
        "                84,89,90]\n",
        "\n",
        "scores=[scorePhysics, scoreLiterature, scoreComputer]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgNEQEbsSYpa",
        "colab_type": "code",
        "outputId": "591ac576-52e4-40ac-9dc9-6686939b101d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        " plt.boxplot(scoreComputer, showmeans=True, whis = 99)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f4c7ac662e8>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f4c7ac66da0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f4c7ac72198>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f4c7ac72c88>],\n",
              " 'means': [<matplotlib.lines.Line2D at 0x7f4c7ac72908>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f4c7ac72550>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f4c7ac66630>,\n",
              "  <matplotlib.lines.Line2D at 0x7f4c7ac669e8>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BMQUAUEBMAQAUEFMAAAXEFABAATEFAFBgUn/o+Pjx47nnnnvy3HPPpb29PVdddVXuvvvu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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98qewxJdO91s",
        "colab_type": "code",
        "outputId": "775be290-62a6-48a6-ce3c-9b00bcb46faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        }
      },
      "source": [
        "box = plt.boxplot(scores, showmeans=True, whis=99)\n",
        "\n",
        "plt.setp(box['boxes'][0], color='blue')\n",
        "plt.setp(box['caps'][0], color='blue')\n",
        "plt.setp(box['caps'][1], color='blue')\n",
        "plt.setp(box['whiskers'][0], color='blue')\n",
        "plt.setp(box['whiskers'][1], color='blue')\n",
        "\n",
        "plt.setp(box['boxes'][1], color='red')\n",
        "plt.setp(box['caps'][2], color='red')\n",
        "plt.setp(box['caps'][3], color='red')\n",
        "plt.setp(box['whiskers'][2], color='red')\n",
        "plt.setp(box['whiskers'][3], color='red')\n",
        "\n",
        "plt.ylim([20, 95]) \n",
        "plt.grid(True, axis='y')  \n",
        "plt.title('Distribution of the scores in three subjects', fontsize=18) \n",
        "plt.ylabel('Total score in that subject')            \n",
        "plt.xticks([1,2,3], ['Physics','Literature','Computer'])\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evLk++ugjhYSE6PDhw3r00Uf1448/6oknnlDv3r0VERGhdevWafXq1frggw8K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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}